Significance Statement

Electric vehicles have been identified to curtail emissions and reduce overreliance on the world’s petroleum supply. In the quest for electric vehicles to cover long distances, powerful batteries have to be incorporated in the vehicles. Therefore, there comes a need to accurately estimate the LiFePO4 battery state of charge in a bid to ensure vehicle reliability and stability.

A battery’s state of charge displays the remaining percentage of its capacity, and its estimation has received significant research interests. In general, the model-based state of charge estimators have been more attractive as opposed to the open loop methods, which include the current integral, since they can circumvent issues with error accumulation and uncertain initial states. Model-based methods, implementing a model to simulate the battery system, adjust the internal model states online reference to the error between the model output and actual measurements. With a more accurate model, the internal model states that are estimated can be used to describe the real system states well.

Luenberger and extended Kalman filter methods are a few model-based methods that have been developed. These methods implement mathematical methods to attenuate noises and approximate model parameters, battery states, and sensor drifting. Unfortunately, there exists an inherent limitation that the model or sensor accuracy must be guaranteed.

Researchers led by professor Furong Gao from The Hong Kong University of Science and Technology proposed a multi-gain observer that was robust to modelling sensor drifts and modelling inaccuracy in a bid to estimate the state of charge. A classifier is employed to switch the gains of the proposed multi-gain observer. Their work is published in peer-reviewed journal, Applied Energy.

In order to pick the suitable feedback gain to estimate the state, the authors need to evaluate the causes of the state of charge estimation error. However, in the state estimation problem, a referenced value can be obtained only when an accurate lab equipment was available, meaning that the authors could not obtain referenced state of charge and state of charge error in practice. Therefore, the authors were compelled to design a classifier that could classify the state of charge error by implementing the error between the real voltage measurement and model output. This means that they used the voltage error to classify the state of charge error. They categorized the error sources into five types: the zero-mean sensing noise, modeling errors, normal error, unclear error, and large state error. Countermeasures are proposed for each error type, and five tests are performed to confirm the proposed approach.

The five tests covered all the considered errors the authors designed in the classifier. Throughout the tests, the classifier as well as the corresponding countermeasures proved to be effective. Instead of correcting the model deviations and sensor drifting by implementing a feedback method, the proposed approach only focused on obtaining a precise state of charge through the gain classified observer, and did nothing to the imprecise sensor or model. This method is a type of feedforward method.

The feedback gain obtained in this manner can also be applied in fault diagnosis in a BMS, for instance, the model accuracy and sensor fault can be classified easily if the feedback gain sequence is identified.

About The Author

Xiaopeng TANG was born in city of Dalian, Liaoning Province, China in 1993. He received bachelor’s degree in automation from University of Science and Technology of China in 2015 under the supervision of Prof. Zonghai Chen. And he is now a Ph.D. Candidate of Department of Chemical and Biological Engineering in Hong Kong University of Science and Technology under the supervision of Prof. Furong GAO.

His recent research has focused on key issues of battery management system, especially the state estimation and prediction problems. He is also participating an entrepreneurship project focusing on decreasing the price of distributed solar energy storage systems and low-speed electric vehicles by using the power batteries retired from electric vehicles.